Browsing by Author "Yang, Ming-Hsun"

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A Neyman-Pearson Type Sensor Censoring Scheme for Compressive Distributed Sparse Signal Recovery
(2018-01-01)
Wu, Jwo-Yuh
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Yang, Ming-Hsun
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Wang, Tsang-Yi
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電機工程學系
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電信工程研究所
To strike a balance between energy efficiency and data quality control, this paper proposes a Neyman-Pearson type sensor censoring scheme for distributed sparse signal recovery via compressive-sensing based on wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor (i) directly transmits the real-valued compressed data if the sensing vector support is detected to be overlapped with the signal support, (ii) sends a one-bit hard decision if empty support overlap is inferred, (iii) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. Computer simulations are used to illustrate the performance of the proposed scheme.
A Sliding Window Approach for Dynamic Event-Region Detection in Sensor Networks
(2014-01-01)
Wang, Tsang-Yi
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Yang, Ming-Hsun
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Wu, Jwo-Yuh
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電機工程學系
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Department of Electrical and Computer Engineering
Event region detection is an important problem in wireless sensor networks. However, a fundamental assumption required for the event-region detection schemes proposed in the literature is that the event-region under detection is quasi-static, i.e., the event-regions do not change during the detection period. This assumption could fail in detecting fast varying events, such as forest fire events and mudslides. Therefore, the detection task considered in the present study is to cope with the continuously varying event regions. Specifically, we adopt a sliding window approach, in which only the sensor measurements from the present to a fixed length time ago are utilized for making decision at any particular time slot. Our simulation results demonstrate the advantages of the proposed detection scheme.
A Space-Time Fusion Scheme for Dynamic-Event Region Detection in Sensor Networks
(2016-01-01)
Wang, Tsang-Yi
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Yang, Ming-Hsun
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Wu, Jwo-Yuh
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電機工程學系
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Department of Electrical and Computer Engineering
Collaborative detection for continuously-varying event-region scenarios using wireless sensor networks (WSNs) has attracted much attention recently. However, most existing works adopt either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive data exchange takes place among the sensors by means of real-value data communications. By contrast, the present study proposes a distributed dynamic event-region detection scheme for WSNs. The proposed scheme is based on a two-phase cooperative space-time decision fusion protocol. In Phase I, each sensor makes an initial decision in accordance with its Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions in accordance with the decision information received from their neighboring nodes. The performance of the proposed scheme is compared with that of a semi-centralized detection scheme by means of computer simulations.
COLLABORATIVE SENSOR CACHING VIA SEQUENTIAL COMPRESSED SENSING
(2019-01-01)
Yang, Yi-Jen
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Yang, Ming-Hsun
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Hong, Y-W Peter
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Wu, Jwo-Yuh
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電信工程研究所
This work proposes a collaborative sensor caching and data reconstruction method based on the sequential compressed sensing framework. Here, multiple caches are assumed to exist in the wireless sensor network to store the most recent data gathered from sensors within their respective coverage areas. To reduce the cache size and the data-acquisition overhead, each cache accesses measurements only from a small subset of sensors. This work proposes a collaborative sparse-signal reconstruction method that exploits the presence of sensors simultaneously accessible by multiple caches as anchor nodes to introduce dependency in the reconstruction. The reconstruction is based on the use of the alternating direction method of multipliers (ADMM), which enables distributed implementation of the algorithm. Simulations are provided to demonstrate the effectiveness of the proposed scheme.
Distributed Detection of Dynamic Event Regions in Sensor Networks With a Gibbs Field Distribution and Gaussian Corrupted Measurements
(2016-09)
Wang, Tsang-Yi
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Yang, Ming-Hsun
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Wu, Jwo-Yuh
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電機學院
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College of Electrical and Computer Engineering
Various methods have been proposed for monitoring continuously varying event-region scenarios using wireless sensor networks (WSNs). However, these methods use either a centralized approach, in which a powerful control center is used to reconstruct the entire random field, or a semi-centralized approach, in which extensive real-value data exchange takes place among the sensors. By contrast, this paper proposes a two-phase distributed dynamic event-region detection scheme for WSNs characterized by a space-time Markov random field with a particular Gibbs distribution. In Phase I of the proposed scheme, each sensor makes an initial decision in accordance with its current Gaussian corrupted observation and previous decision. In Phase II, the nodes update their decisions based on the decision information received from their neighbors. Notably, the proposed scheme has a low-communication-rate characteristic, and is thus ideally suited to WSN applications. The performance of the proposed scheme is compared with that of a semicentralized detection scheme by means of computer simulations.
Energy-Efficient Sensor Censoring for Compressive Distributed Sparse Signal Recovery
(2018-05-01)
Wu, Jwo-Yuh
;
Yang, Ming-Hsun
;
Wang, Tsang-Yi
;
電信工程研究所
;
Institute of Communications Engineering
To strike a balance between energy efficiency and data quality control, this paper proposes a sensor censoring scheme for distributed sparse signal recovery via compressive-sensing-based wireless sensor networks. In the proposed approach, each sensor node employs a sparse sensing vector with known support for data compression, meanwhile enabling making local inference about the unknown support of the sparse signal vector of interest. This naturally leads to a ternary censoring protocol, whereby each sensor: 1) directly transmits the real-valued compressed data, if the sensing vector support is detected to be overlapped with the signal support; 2) sends a one-bit hard decision if empty support overlap is inferred; and 3) keeps silent if the measurement is judged to be uninformative. Our design then aims at minimizing the error probability that empty support overlap is decided but otherwise is true, subject to the constraints on a tolerable false-alarm probability that non-empty support overlap is decided but otherwise is true, and a target censoring rate. We derive a closed-form formula of the optimal censoring rule; a low complexity implementation using bi-section search is also developed. In addition, the average communication cost is analyzed. To aid global signal reconstruction under the proposed censoring framework, we propose a modified l(1)-minimization based algorithm, which exploits certain sparse nature of the hard decision vector received at the fusion center. Analytic performance guarantees, characterized in terms of the restricted isometry property, are also derived. Computer simulations are used to illustrate the performance of the proposed scheme.
Enhanced Noisy Sparse Subspace Clustering via Reweighted L1-Minimization
(2018-01-01)
Wu, Jwo-Yuh
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Huang, Liang-Chi
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Yang, Ming-Hsun
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Chang, Ling-Hua
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Liu, Chun-Hung
Sparse subspace clustering (SSC) relies on sparse regression for accurate neighbor identification. Inspired by recent progress in compressive sensing (CS), this paper proposes a new sparse regression scheme for SSC via reweighted l(1)-minimization, which also generalizes a twostep l(1)-minimization algorithm introduced by E. J. Candes al all in [The Annals of Statistics, vol. 42, no. 2, pp. 669-699, 2014] without incurring extra complexity burden. To fully exploit the prior information conveyed by the computed sparse vector in the first step, our approach places a weight on each component of the regression vector, and solves a weighted LASSO in the second step. We discuss the impact of weighting on neighbor identification, argue that a popular weighting rule used in CS literature is not suitable for the SSC purpose, and propose a new weighting scheme for enhancing neighbor identification accuracy. Extensive simulation results are provided to validate our discussions and evidence the effectiveness of the proposed approach. Some key issues for future works are also highlighted.
Fusion-Based Cooperative Support Identification for Compressive Networked Sensing
(2020-02-01)
Yang, Ming-Hsun
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Wu, Jwo-Yuh
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Wang, Tsang-Yi
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Maunder, Robert G.
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Gau, Rung-Hung
This letter proposes a fusion-based cooperative support identification scheme for distributed compressive sparse signal recovery via resource-constrained wireless sensor networks. The proposed support identification protocol involves: (i) local sparse sensing for economizing data gathering and storage, (ii) local binary decision making for partial support knowledge inference, (iii) binary information exchange among active nodes, and (iv) binary data aggregation for support estimation. Then, with the aid of the estimated signal support, a refined local decision is made at each node. Only the measurements of those informative nodes will be sent to the fusion center, which employs a weighted l(1)-minimization for global signal reconstruction. The design of a Bayesian local decision rule is discussed, and the average communication cost is analyzed. Computer simulations are used to illustrate the effectiveness of the proposed scheme.
Low-Latency Compressive Active User Identification over Frequency-Selective Fading Channels
(2018-01-01)
Chang, Chun-Yi
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Wu, Jwo-Yuh
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Yang, Ming-Hsun
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Wang, Tsang-Yi
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Shao, Shuai
This paper proposes a compressive sensing (CS) based active user identification scheme over frequency-selective fading channels. Unlike the conventional cyclic prefix (CP) based preamble transmission, our approach does not utilize CP in order to conserve signaling overhead, in turn reducing the system-wide processing latency. Our approach first estimates the multi-user channel impulse response vectors by solving a mixed l(2 )/ l(1) - norm optimization problem; then, the active users are identified via a sorting of the norms of the estimated channel vectors. By exploiting the Toeplitz channel matrix structure resulting from CP-free preamble transmission, analytic performance guarantee in term of the block restricted isometry property of the preamble matrix is given. Computer simulations are used to illustrate the performance of the proposed method.
Malicious Crowdsourcing Worker Detection using Privacy-Aware Group Queries
(2019-01-01)
Yang, Ming-Hsun
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Hong, Y. -W. Peter
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Wang, Tsang-Yi
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Wu, Jwo-Yuh
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電機工程學系
This work proposes efficient methods for the detection of malicious crowdsourcing workers using only privacy-aware group queries. In the proposed system, the crowdsourcing platform first issues a series of standard tasks to the workers, and allows users (i.e., data owners) to access aggregate responses from the workers through group queries that can be described by sparse encoding vectors. The identities of workers associated with individual responses are not explicitly revealed. By exploiting the sparse nature of the encoding vectors, we first propose an approximate maximum a posteriori probability (Approx. MAP) detector to perform the detection. Then, to further reduce computational complexity, we devise a generalized likelihood ratio test (GLRT) where probable malicious workers are first identified before a simple hypothesis test is performed. The identification of malicious workers is performed by a low-complexity probability-based rule that exploits a certain sparse structure inherent in the crowd data as well as the associated statistical assumptions. Computer simulations show that the proposed methods outperform the conventional energy detector.
SPARSE SUBSPACE CLUSTERING WITH SEQUENTIALLY ORDERED AND WEIGHTED L1-MINIMIZATION
(2019-01-01)
Wu, Jwo-Yuh
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Huang, Liang-Chi
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Yang, Ming-Hsun
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Chang, Ling-Hua
;
Liu, Chun-Hung
Built on the sparse representation framework, sparse subspace clustering (SSC) received considerable attention in the recent years. Conventional SSC employs. l(1)-minimization based sparse regression for neighbor identification on a sample-by-sample basis, and is unaware of the neighbor information revealed by those already computed sparse representation vectors. To rid this drawback, this paper proposes a weighted. l(1)-minimization based sparse regression method, and an associated data ordering rule able to reflect the reliability of neighbor information for further enhancing the clustering accuracy. The selection of weighting coefficients for SSC is also discussed. Computer simulations using both the synthesis and real data are provided to evidence the effectiveness of the proposed method.